It's possible that many people (like me) had been trying to solve a similar non-business problem for years, including rolling our own solutions and the like, and when an open source option widely backed emerged and looked like a possible standard, we accept some warts in exchange for a broad, general purpose, flexible, automatable, and well-thought out (yes) solution.

Of course K8s is not perfect, and it's overkill for small to medium apps (I think hype train convinced a lot of people they would need to scale to massive cloud levels when really they didn't), but if you have ever needed K8s (especially for a complex microservice system at big enterprise level) then you know the value and you remember the proprietary vendor-locked era of sadness before K8s emerged.

and me)

Engineering-driven-development, where the purpose of engineering is engineering and business problems take a backseat in favor of hiring lots of engineers, giving them toys to play with and having buzzwords to on your careers page. In this case, your objective is to fill up your stack with as many shiny things as possible regardless of whether they are necessary or appropriate to solve the business problem at hand.

This strongly correlates with bullshit VC-funded companies that wouldn't be profitable even on a good day so their only objective is to raise as much funding as possible (the business problem is secondary, because even solving the business problem perfectly wouldn't make them profitable) and chasing fads and buzzwords is sadly a valid strategy for raising capital.

There is a cost for not getting on a hype train. CEOs rather waste a couple of billion dollars on that machine learning/AI hype train, because if they don't hop on that train, Wall street folks see these companies old-school, not innovating. So, they bring in Chief AI officer and write about about how machine learning gonna help their companies in the Quarterly reports. Even if they lose $2B in this AI venture, just treat as insurance premium to keep the stock price going up.

Same with all these technologies.

AI is cargo-culting from executives in publicly traded companies.

Kubernetes is a grassroots phenomenon of developers experimenting with self-immolation while padding the resume.

Major difference. Your example comes from the top. Kubernetes comes from the bottom, and is more insidious in my opinion.

This. I cannot tell you the number of times where I've seen teams use Mongo and then reinvent a schema as part of the development contracts. It's enraging.

Hype train tickets are cheap to get on, but expensive to get off.

Since hackernews is mostly junior devs or senior principle architects (formerly junior devs), I expect there's a lot of stockholm syndrome going on in these comments.

Better get out of here before they notice us.

Well, I am using it as my PO wants it, to serve only 4 apis.